Information Filtering via Improved Similarity Definition

基于用户类似的一个新定义，我们介绍一改进合作过滤(ICF ) 算法，它能同时改进算法的精确性和差异。在 ICF，而不是标准皮尔森系数，用户用户类似被集成热传导和集体散开过程获得。一个基准数据集合上的模拟结果显示相应算法的精确性，在评价分数测量了，被 6.7% 与标准相比在最佳的盒子中改进合作过滤(CF ) 算法。更重要地，建议表的差异被 63.6% 也改进。因为用户类似为 CF 算法是关键的，这个工作可以阐明怎么由给精确类似测量改进算法的表演。[从作者抽象]

Based on a new definition of user similarity, we introduce an improved collaborative filtering （ICF） algorithm, which couM improve the algorithmic accuracy and diversity simultaneously. In the ICF, instead of the standard Pearson coefficient, the user-user similarities are obtained by integrating the heat conduction and mass diffusion processes. The simulation results on a benchmark data set indicate that the corresponding algorithmic accuracy, measured by the ranking score, is improved by 6. 7% in the optimal case compared to the standard collaborative filtering （CF） algorithm. More importantly, the diversity of the recommendation lists is also improved by 63.6%. Since the user similarity is crucial for the CF algorithm, this work may shed some light on how to improve the algorithmic performance by giving accurate similarity measurement.